Industry
Preparing data for supervised fine-tuning Part 2: Advanced data strategies
The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data w
The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.
Related
- Preparing data for supervised fine-tuning Part 1: Formatting and quality
- Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
Source: AWS ML Blog | 2026-08-26